MIT’s introductory course on deep learning methods with applications in gameplay, and more!

MIT’s introductory course on deep learning methods with applications to computer vision, natural language processing, biology, and more!

Students will get a foundational knowledge of deep learning algorithms and get practical experience in building neural networks in TensorFlow. The course ends with a project proposal competition with feedback from staff and a panel of industry sponsors.

MIT Introduction to Deep Learning 6.S191: Lecture 1
Foundations of Deep Learning
Lecturer: Alexander Amini

MIT Introduction to Deep Learning 6.S191: Lecture 2
Recurrent Neural Networks
Lecturer: Ava Soleimany

MIT Introduction to Deep Learning 6.S191: Lecture 3
Convolutional Neural Networks for Computer Vision
Lecturer: Alexander Amini

MIT 6.S191 (2021): Introduction to Deep Learning: Lecture 4
Deep Generative Modeling
Lecturer: Ava Soleimany

MIT Introduction to Deep Learning 6.S191: Lecture 5
Deep Reinforcement Learning
Lecturer: Alexander Amini

MIT Introduction to Deep Learning 6.S191: Lecture 6
Deep Learning Limitations and New Frontiers
Lecturer: Ava Soleimany

MIT Introduction to Deep Learning 6.S191: Lecture 7
Evidential Deep Learning and Uncertainty Estimation
Lecturer: Alexander Amini

MIT Introduction to Deep Learning 6.S191: Lecture 8
Algorithmic Bias and Fairness
Lecturer: Ava Soleimany

MIT Introduction to Deep Learning 6.S191: Lecture 9
Deep CPCFG for Information Extraction
Lecturer: Nigel Duffy and Freddy Chua, Ernst & Young AI Labs

MIT Introduction to Deep Learning 6.S191: Lecture 10
Taming Dataset Bias via Domain Adaptation
Lecturer: Prof. Kate Saenko, MIT-IBM Watson AI Lab

MIT Introduction to Deep Learning 6.S191: Lecture 11
Towards AI for 3D Content Creation
Lecturer: Prof. Sanja Fidler, University of Toronto and NVIDIA